Marcus, a Detroit autoworker, successfully transitioned to logistics coordination after plant automation. He learned inventory management, customer service, and digital tools on the job\u2014valuable, transferable skills. But there\u2019s no systematic record of that transition or those competencies.<\/p>\n
Stories like Marcus\u2019s are increasingly common among manufacturing workers who\u2019ve shifted to logistics, healthcare support, and technical services. These transitions represent exactly the kind of career adaptability our economy needs. Yet we have no way to identify, track, or replicate these successful pathways.<\/p>\n
That\u2019s not just a data problem\u2014it\u2019s a policy failure that leaves millions of workers invisible to the very systems designed to support them.<\/p>\n
When the pandemic struck in 2020, this invisibility became devastating. Sarah, a freelance graphic designer in Chicago, discovered that five years of steady client work meant nothing to unemployment systems that couldn\u2019t see her employment history. Like millions of other Americans in the gig economy, her contributions were invisible to emergency relief systems.<\/p>\n
That failure wasn\u2019t a fluke\u2014it revealed a structural problem. Our employment data systems are fundamentally misaligned with today\u2019s economy. And as the pace of economic change accelerates, this infrastructure gap threatens to undermine workforce policies across the board.<\/p>\n
The foundation we\u2019re missing<\/strong><\/p>\n For all the investment in skills-first hiring and innovative credentials, we\u2019ve overlooked something basic: we don\u2019t systematically track work. We meticulously record degrees and licenses, but we fail to capture who did what job, where, for how long, or with what outcomes.<\/p>\n Think of it this way: Credentials say what you know, employment records show what you\u2019ve done.<\/p>\n \u201cAlmost 40 percent of global employment is exposed to AI, with advanced economies at greater risk [\u2026] due to prevalence of cognitive-task-oriented jobs\u201d, according to the IMF\u2019s 2024 Gen-AI Report<\/a>. Those workers need to be trained to switch occupational categories, but which of these transitions are more likely to succeed?<\/p>\n Without comprehensive employment records, we\u2019re constructing workforce policy on quicksand. If we can\u2019t track experience, we can\u2019t recognize it, reward it, enhance it, or build on it.<\/p>\n And, as I recently argued here<\/a>, experience will soon be our last chance to complement the algorithm.<\/p>\n Built for yesterday\u2019s economy<\/strong><\/p>\n Our employment record infrastructure reflects 20th-century assumptions: one job, one employer, one career arc. That world has vanished.<\/p>\n Today\u2019s workers navigate frequent job transitions, patch together income from multiple sources, and acquire skills continuously. The labor market has become modular, but our data systems remain monolithic. More than 18 million Americans now work as independent contractors, on-call staff, or through temp and contract firms\u2014forms of employment often invisible in standard labor data, as documented by the Contingent Work Supplement to the Current Population Survey<\/a>.<\/p>\n This creates cascading problems. Workers lose opportunities for recognition and advancement when their experience isn\u2019t systematically recorded. Policymakers operate on incomplete information, making decisions about workforce programs without knowing what actually works. Employers face inefficient hiring processes because reliable work histories don\u2019t exist.<\/p>\n The future demands better data<\/strong><\/p>\n The economic transformation underway makes comprehensive employment records an essential infrastructure, not an administrative convenience.<\/p>\n Consider what becomes possible with systematic employment tracking. Instead of guessing which career paths work, we could analyze patterns from thousands of workers like Marcus\u2014identifying which manufacturing workers successfully move to healthcare support, what skills predict success in logistics coordination, and how different training programs affect career outcomes.<\/p>\n Community colleges and workforce programs could track whether their graduates actually advance in their careers, not just whether they complete certificates.\u00a0Germany\u2019s system already does this, linking training, wages, and employment histories in longitudinal datasets<\/a> that power robust policy evaluation and real-world outcome tracking.<\/p>\n Rather than relying on quarterly surveys that lag months behind reality, labor market intelligence could flow in real-time. State workforce agencies could identify emerging shortages as they develop, not after they become crises. By analyzing successful transitions of similar workers, recommendation systems could suggest career paths that match individual experience profiles. LinkedIn\u2019s Economic Graph<\/a> illustrates the potential of this approach, though current insights are based on aggregated data and depend on users with complete profiles.<\/p>\n Without public investment in employment records, control over this capability remains with private platforms, reinforcing existing inequalities in data access and career guidance.<\/p>\n What works elsewhere<\/strong><\/p>\n Other nations are demonstrating what\u2019s possible when employment records become true public infrastructure:<\/p>\n India\u2019s e-SHRAM platform<\/strong> has registered over 300 million informal workers<\/a> through a combination of mobile technology and in-person registration, bringing a massive, previously invisible workforce into view\u2014and into social protection programs. Beyond connecting workers to social protection schemes, e-SHRAM is also integrated with platforms like the National Career Service (NCS) and the Skill India Digital Hub (SIDH), helping link informal workers to job opportunities, career counseling, and targeted training programs.<\/p>\n The United Kingdom\u2019s real-time payroll system<\/strong> integrates income reporting into the PAYE tax infrastructure, requiring employers to submit payroll data each pay cycle. This data flows to the Department for Work and Pensions multiple times per day<\/a>, enabling timely benefit adjustments and reducing tax credit overpayments through accurate, real-time earnings information.<\/p>\n Germany\u2019s modular integration<\/strong> of wage, benefits, and job search data through the Integrated Employment Biographies (IEB) system creates one of the most advanced labor data infrastructures in the world. The IEB operates at daily frequency and merges inputs from five administrative sources<\/a>, enabling granular tracking of employment histories, improving program delivery, enabling real-time labor market monitoring, and powering policy evaluation.<\/p>\n Estonia\u2019s employment register<\/strong>, part of the country\u2019s globally recognized e-government ecosystem<\/a> (e-Estonia<\/a>), records employment relationships in the real time and automatically shares data across tax, health, and social systems via the X-Road open-source software<\/a>. While it doesn\u2019t track wages or work outcomes, it shows how integrated infrastructure can reduce undeclared work and streamline access to benefits.<\/p>\n These\u2013and other\u2013international systems highlight several shared lessons1<\/a>:<\/p>\n The hidden costs of inaction<\/strong><\/p>\n In the U.S., as in many other economies, the employment record system creates significant inefficiencies: employers spend billions on recruitment and skills verification, workers struggle to signal their true capabilities, and training programs operate without feedback on real-world outcomes.<\/p>\n Workers reasonably worry about creating detailed digital profiles that could enable surveillance or overreach. The key is designing systems with privacy protection built in from the start. Workers should own and control access to their employment records, similar to how medical records work under HIPAA<\/a>, granting permission for specific uses. Policy analysis can rely on aggregated, anonymized data that reveals patterns without exposing individual information. Germany\u2019s system, for example, demonstrates that comprehensive employment records can coexist with strong privacy protections.<\/p>\n Modern employment records don\u2019t just help individual workers\u2014they could transform how labor markets function. Comprehensive employment records create market transparency that benefits everyone. When work histories are portable and verifiable, job matching becomes more precise. Workers can demonstrate concrete experience rather than relying on degree requirements. Employers can identify talent based on actual performance patterns, not just educational credentials.<\/p>\n This transparency becomes increasingly important as work becomes more distributed, skills more specialized, and careers more dynamic. Without action, the gap between our policy ambitions and our data infrastructure will continue to widen.<\/p>\n A U.S. roadmap<\/strong><\/p>\n Building modern employment record infrastructure requires four key elements:<\/p>\n Standards first<\/strong>: Establish a common data model that captures gig work and project-based employment, not just traditional job titles, ensuring different systems can work together seamlessly.<\/p>\n Digital integration<\/strong>: Create streamlined reporting mechanisms that allow payroll providers and platforms to submit data once while feeding multiple government systems\u2014eliminating duplicate reporting.<\/p>\n Inclusive coverage<\/strong>: Design systems that capture all forms of work through platform partnerships where companies like Uber and Upwork report worker activity directly to state systems.<\/p>\n Coordinated governance<\/strong>: Implement federal-state coordination that maintains local flexibility while ensuring national coherence, similar to how Medicaid operates.<\/p>\n Building adaptive capacity<\/strong><\/p>\n As economic change accelerates, our ability to respond effectively depends on having real-time visibility into how that change affects actual workers in real jobs. Employment records are the infrastructure that makes evidence-based workforce policy possible.<\/p>\n For workers, modern employment records mean faster benefit access, better experience recognition, and more relevant career guidance. For employers, they reduce verification costs and improve talent identification. For policymakers, they provide the granular data needed to identify successful reskilling pathways and measure program effectiveness. Women, immigrants, and low-wage workers\u2014disproportionately excluded from current systems\u2014would gain visibility and access to opportunities.<\/p>\n Most fundamentally, this is about building adaptive capacity. We\u2019ve invested heavily in improving how we certify knowledge through badges, certificates, and alternative credentials. Now we must invest equally in tracking and valuing the experience where that knowledge gets applied\u2013an aspect that will be increasingly important in the future.<\/p>\n Employment records aren\u2019t a glamorous policy fix, but they\u2019re a foundational one. We have the technology and the international models. We need the commitment to build.<\/p>\n References<\/p>\n [1] Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J Panton, Carlo Pizzinelli, Emma J Rockall, and Marina Mendes Tavares. \u201cGen-AI: Artificial Intelligence and the Future of Work<\/a>\u201c, Staff Discussion Notes 2024\/001, 2024.<\/p>\n [2] Eduardo Levy Yeyati, \u201cWhy gen AI can\u2019t fully replace us (for now)<\/a>\u201d, Brookings, December 2024.<\/p>\n [3] U.S. Bureau of Labor Statistics, \u201cContingent and Alternative Employment Arrangements \u2013 July 2023<\/a>\u201d, November 2024.<\/p>\n [4] LinkedIn Economic Graph, Workforce data and research<\/a>, 2025.<\/p>\n [5] Ministry of Labour & Employment, E-Shram Dashboard<\/a>, Government of India.<\/p>\n [6] Ministry of Labour & Employment, E-Shram: One Stop Solution for Unorganised Workers<\/a>. Government of India, Press Information Bureau, 2025.<\/p>\n [7] Chartered Institute of Payroll Professionals, Understanding the relationship between RTI and UC<\/a>, Professional in Payrol, Pensions & Reward, Issue 75, p. 25, 2025.<\/p>\n [8] World Bank, GovTech Maturity Index (GTMI)<\/a>.<\/p>\n [9] Rainer Kattel, and Ines Mergel, \u201cEstonia\u2019s digital transformation: Mission mystique and the hiding hand<\/a>\u201d, Working Paper 2018-09, UCL, 2018.<\/p>\n\n